def PyExec(self):
# get parameter values
wsString = self.getPropertyValue("InputWorkspace").strip()
#internal values
wsOutput = "__OutputWorkspace"
wsTemp = "__Sort_temp"
#get the workspace list
wsNames = []
for wsName in wsString.split(","):
ws = mtd[wsName.strip()]
if isinstance(ws, WorkspaceGroup):
wsNames.extend(ws.getNames())
else:
wsNames.append(wsName)
if wsOutput in mtd:
ms.DeleteWorkspace(Workspace=wsOutput)
sortStat = []
for wsName in wsNames:
if "qvectors" in wsName:
#extract the spectrum
ws = mtd[wsName.strip()]
for s in range(0, ws.getNumberHistograms()):
y_s = ws.readY(s)
stuple = (self.GetXValue(y_s), s)
sortStat.append(stuple)
sortStat.sort()
if len(sortStat) == 0:
raise RuntimeError("Cannot find file with qvectors, aborting")
#sort spectra using norm of q
for wsName in wsNames:
ws = mtd[wsName.strip()]
yUnit = ws.getAxis(1).getUnit().unitID()
transposed = False
if ws.getNumberHistograms() < len(sortStat):
ms.Transpose(InputWorkspace=wsName, OutputWorkspace=wsName)
transposed = True
for norm, spec in sortStat:
ms.ExtractSingleSpectrum(InputWorkspace=wsName, OutputWorkspace=wsTemp, WorkspaceIndex=spec)
if wsOutput in mtd:
ms.ConjoinWorkspaces(InputWorkspace1=wsOutput,InputWorkspace2=wsTemp,CheckOverlapping=False)
if wsTemp in mtd:
ms.DeleteWorkspace(Workspace=wsTemp)
else:
ms.RenameWorkspace(InputWorkspace=wsTemp, OutputWorkspace=wsOutput)
#put norm as y value and copy units from input
loopIndex = 0
wsOut = mtd[wsOutput]
for norm, spec in sortStat:
wsOut.getSpectrum(loopIndex).setSpectrumNo(int(norm*1000))
loopIndex = loopIndex + 1
if len(yUnit) > 0:
wsOut.getAxis(1).setUnit(yUnit)
if transposed:
ms.Transpose(InputWorkspace=wsOutput, OutputWorkspace=wsOutput)
ms.RenameWorkspace(InputWorkspace=wsOutput, OutputWorkspace=wsName)
def rebin_workspace(self, input_ws, binning_param_list, output_ws_name):
"""
rebin input workspace with user specified binning parameters
:param input_ws:
:param binning_param_list:
:param output_ws_name:
:return:
"""
if binning_param_list is None:
# no re-binning is required: clone the output workspace
output_workspace = api.CloneWorkspace(
InputWorkspace=input_ws, OutputWorkspace=output_ws_name)
else:
# rebin input workspace
processed_single_spec_ws_list = list()
for ws_index in range(input_ws.getNumberHistograms()):
# rebin on each
temp_out_name = output_ws_name + '_' + str(ws_index)
processed_single_spec_ws_list.append(temp_out_name)
# extract a spectrum out
api.ExtractSpectra(input_ws,
WorkspaceIndexList=[ws_index],
OutputWorkspace=temp_out_name)
# get binning parameter
bin_params = binning_param_list[ws_index]
if bin_params is None:
continue
# rebin
# check
if len(bin_params) % 2 == 0:
# odd number and cannot be binning parameters
raise RuntimeError(
'Binning parameter {0} cannot be accepted.'.format(
bin_params))
api.Rebin(InputWorkspace=temp_out_name,
OutputWorkspace=temp_out_name,
Params=bin_params,
PreserveEvents=True)
rebinned_ws = AnalysisDataService.retrieve(temp_out_name)
self.log().warning(
'Rebinnd workspace Size(x) = {0}, Size(y) = {1}'.format(
len(rebinned_ws.readX(0)), len(rebinned_ws.readY(0))))
# Upon this point, the workspace is still HistogramData.
# Check whether it is necessary to reset the X-values to reference TOF from VDRIVE
temp_out_ws = AnalysisDataService.retrieve(temp_out_name)
if len(bin_params) == 2 * len(temp_out_ws.readX(0)) - 1:
reset_bins = True
else:
reset_bins = False
# convert to point data
api.ConvertToPointData(InputWorkspace=temp_out_name,
OutputWorkspace=temp_out_name)
# align the bin boundaries if necessary
temp_out_ws = AnalysisDataService.retrieve(temp_out_name)
if reset_bins:
# good to align:
for tof_i in range(len(temp_out_ws.readX(0))):
temp_out_ws.dataX(0)[tof_i] = int(
bin_params[2 * tof_i] * 10) / 10.
# END-FOR (tof-i)
# END-IF (align)
# END-FOR
# merge together
api.RenameWorkspace(
InputWorkspace=processed_single_spec_ws_list[0],
OutputWorkspace=output_ws_name)
for ws_index in range(1, len(processed_single_spec_ws_list)):
api.ConjoinWorkspaces(
InputWorkspace1=output_ws_name,
InputWorkspace2=processed_single_spec_ws_list[ws_index])
# END-FOR
output_workspace = AnalysisDataService.retrieve(output_ws_name)
# END-IF-ELSE
return output_workspace
def fit_tof_iteration(sample_data, container_data, runs, flags):
"""
Performs a single iterations of the time of flight corrections and fitting
workflow.
:param sample_data: Loaded sample data workspaces
:param container_data: Loaded container data workspaces
:param runs: A string specifying the runs to process
:param flags: A dictionary of flags to control the processing
:return: Tuple of (workspace group name, pre correction fit parameters,
final fit parameters, chi^2 values)
"""
# Transform inputs into something the algorithm can understand
if isinstance(flags['masses'][0], list):
mass_values = _create_profile_strs_and_mass_list(
copy.deepcopy(flags['masses'][0]))[0]
profiles_strs = []
for mass_spec in flags['masses']:
profiles_strs.append(
_create_profile_strs_and_mass_list(mass_spec)[1])
else:
mass_values, profiles_strs = _create_profile_strs_and_mass_list(
flags['masses'])
background_str = _create_background_str(flags.get('background', None))
intensity_constraints = _create_intensity_constraint_str(
flags['intensity_constraints'])
ties = _create_user_defined_ties_str(flags['masses'])
num_spec = sample_data.getNumberHistograms()
pre_correct_pars_workspace = None
pars_workspace = None
fit_workspace = None
max_fit_iterations = flags.get('max_fit_iterations', 5000)
output_groups = []
chi2_values = []
data_workspaces = []
result_workspaces = []
group_name = runs + '_result'
for index in range(num_spec):
if isinstance(profiles_strs, list):
profiles = profiles_strs[index]
else:
profiles = profiles_strs
suffix = _create_fit_workspace_suffix(index, sample_data,
flags['fit_mode'],
flags['spectra'],
flags.get('iteration', None))
# Corrections
corrections_args = dict()
# Need to do a fit first to obtain the parameter table
pre_correction_pars_name = runs + "_params_pre_correction" + suffix
corrections_fit_name = "__vesuvio_corrections_fit"
ms.VesuvioTOFFit(InputWorkspace=sample_data,
WorkspaceIndex=index,
Masses=mass_values,
MassProfiles=profiles,
Background=background_str,
IntensityConstraints=intensity_constraints,
Ties=ties,
OutputWorkspace=corrections_fit_name,
FitParameters=pre_correction_pars_name,
MaxIterations=max_fit_iterations,
Minimizer=flags['fit_minimizer'])
ms.DeleteWorkspace(corrections_fit_name)
corrections_args['FitParameters'] = pre_correction_pars_name
# Add the multiple scattering arguments
corrections_args.update(flags['ms_flags'])
corrected_data_name = runs + "_tof_corrected" + suffix
linear_correction_fit_params_name = runs + "_correction_fit_scale" + suffix
if flags.get('output_verbose_corrections', False):
corrections_args[
"CorrectionWorkspaces"] = runs + "_correction" + suffix
corrections_args[
"CorrectedWorkspaces"] = runs + "_corrected" + suffix
if container_data is not None:
corrections_args["ContainerWorkspace"] = container_data
ms.VesuvioCorrections(
InputWorkspace=sample_data,
OutputWorkspace=corrected_data_name,
LinearFitResult=linear_correction_fit_params_name,
WorkspaceIndex=index,
GammaBackground=flags.get('gamma_correct', False),
Masses=mass_values,
MassProfiles=profiles,
IntensityConstraints=intensity_constraints,
MultipleScattering=True,
GammaBackgroundScale=flags.get('fixed_gamma_scaling', 0.0),
ContainerScale=flags.get('fixed_container_scaling', 0.0),
**corrections_args)
# Final fit
fit_ws_name = runs + "_data" + suffix
pars_name = runs + "_params" + suffix
fit_result = ms.VesuvioTOFFit(
InputWorkspace=corrected_data_name,
WorkspaceIndex=0,
Masses=mass_values,
MassProfiles=profiles,
Background=background_str,
IntensityConstraints=intensity_constraints,
Ties=ties,
OutputWorkspace=fit_ws_name,
FitParameters=pars_name,
MaxIterations=max_fit_iterations,
Minimizer=flags['fit_minimizer'])
chi2_values.append(fit_result[-1])
ms.DeleteWorkspace(corrected_data_name)
# Process parameter tables
if pre_correct_pars_workspace is None:
pre_correct_pars_workspace = _create_param_workspace(
num_spec, mtd[pre_correction_pars_name])
if pars_workspace is None:
pars_workspace = _create_param_workspace(num_spec, mtd[pars_name])
if fit_workspace is None:
fit_workspace = _create_param_workspace(
num_spec, mtd[linear_correction_fit_params_name])
spec_num_str = str(sample_data.getSpectrum(index).getSpectrumNo())
current_spec = 'spectrum_' + spec_num_str
_update_fit_params(pre_correct_pars_workspace, index,
mtd[pre_correction_pars_name], current_spec)
_update_fit_params(pars_workspace, index, mtd[pars_name], current_spec)
_update_fit_params(fit_workspace, index,
mtd[linear_correction_fit_params_name],
current_spec)
ms.DeleteWorkspace(pre_correction_pars_name)
ms.DeleteWorkspace(pars_name)
ms.DeleteWorkspace(linear_correction_fit_params_name)
# Process spectrum group
# Note the ordering of operations here gives the order in the WorkspaceGroup
output_workspaces = []
data_workspaces.append(fit_ws_name)
if flags.get('output_verbose_corrections', False):
output_workspaces += mtd[
corrections_args["CorrectionWorkspaces"]].getNames()
output_workspaces += mtd[
corrections_args["CorrectedWorkspaces"]].getNames()
ms.UnGroupWorkspace(corrections_args["CorrectionWorkspaces"])
ms.UnGroupWorkspace(corrections_args["CorrectedWorkspaces"])
for workspace in output_workspaces:
group_name = runs + '_iteration_' + str(
flags.get('iteration', None))
name = group_name + '_' + workspace.split(
'_')[1] + '_' + workspace.split('_')[-1]
result_workspaces.append(name)
if index == 0:
ms.RenameWorkspace(InputWorkspace=workspace,
OutputWorkspace=name)
else:
ms.ConjoinWorkspaces(InputWorkspace1=name,
InputWorkspace2=workspace)
# Output the parameter workspaces
params_pre_corr = runs + "_params_pre_correction_iteration_" + str(
flags['iteration'])
params_name = runs + "_params_iteration_" + str(flags['iteration'])
fit_name = runs + "_correction_fit_scale_iteration_" + str(
flags['iteration'])
AnalysisDataService.Instance().addOrReplace(
params_pre_corr, pre_correct_pars_workspace)
AnalysisDataService.Instance().addOrReplace(params_name,
pars_workspace)
AnalysisDataService.Instance().addOrReplace(fit_name, fit_workspace)
if result_workspaces:
output_groups.append(
ms.GroupWorkspaces(InputWorkspaces=result_workspaces,
OutputWorkspace=group_name))
if data_workspaces:
output_groups.append(
ms.GroupWorkspaces(InputWorkspaces=data_workspaces,
OutputWorkspace=group_name + '_data'))
else:
output_groups.append(fit_ws_name)
if len(output_groups) > 1:
result_ws = output_groups
else:
result_ws = output_groups[0]
return result_ws, pre_correct_pars_workspace, pars_workspace, chi2_values